Abstract: High-throughput data technologies have led to an explosion of interest in reconstructing biological networks. Dynamic models based on systems of coupled ordinary differential equations (ODEs) not only allow for reconstruction from time-course data, but also provide additional insight into the underlying biological mechanisms relative to methods based on steady state data. In this work I formalize the network reconstruction problem from a dynamic systems point of view and propose a novel coupling metric for quantifying the relationship between coupled nonlinear ODEs. Combining existing techniques in a novel way, methodology is developed for non-parametric estimation of a dynamic system. The methodology is illustrated using data from an in silico networks describing the regulatory relationships among transcription factors in mouse-embryonic stem cells. In a second example using an in silico model of an E. coli subnetwork I show how the model can be modified to accommodate varied experimental conditions.